Guest editors’ introduction: special issue on Inductive Logic Programming and on Multi-Relational Learning
Probabilistic (logic) programming concepts
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited
Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases
Efficient inference and learning in a large knowledge base
Bandit-based Monte-Carlo structure learning of probabilistic logic programs
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track
Direct conditional probability density estimation with sparse feature selection
Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge
Learning relational dependency networks in hybrid domains
Policy gradient in Lipschitz Markov Decision Processes
A Bayesian approach for comparing cross-validated algorithms on multiple data sets
Soft-max boosting
Minimum message length estimation of mixtures of multivariate Gaussian and von Mises-Fisher distributions
Consensus hashing
Generalized Twin Gaussian processes using Sharma–Mittal divergence
Improving classification performance through selective instance completion
Optimised probabilistic active learning (OPAL)
Poisson Dependency Networks: Gradient Boosted Models for Multivariate Count Data
A decomposition of the outlier detection problem into a set of supervised learning problems
Convex relaxations of penalties for sparse correlated variables with bounded total variation
Incremental learning of event definitions with Inductive Logic Programming
Generalized gradient learning on time series
Learning from evolving video streams in a multi-camera scenario
Probabilistic clustering of time-evolving distance data
Regularized feature selection in reinforcement learning
Half-space mass: a maximally robust and efficient data depth method
